Automatic path generation device

ABSTRACT

An automatic path generation device includes a preprocessing unit creating teacher data based on a temporary motion path which is a motion path between a plurality of motion points where a robot moves and which is automatically generated with a motion planning algorithm and an actual motion path which is a motion path between the motion points and which is created by a skilled worker and a motion path learning unit generating a learned model which has learned a difference between the temporary motion path and the actual motion path with teacher data created by the preprocessing unit.

RELATED APPLICATIONS

The present application claims priority to Japanese Patent ApplicationNumber 2018-134515 filed Jul. 17, 2018, the disclosure of which ishereby incorporated by reference herein in its entirety.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present disclosure relates to an automatic path generation device.

2. Description of the Related Art

The motion path at a time when a robot performing spot welding or arcwelding moves (time series data indicating, for example, the coordinatevalue and the velocity of each axis of the robot determining a weldingtool position or a welding tool posture) is generated by teaching from aworker. In some cases, the robot's motion path is automaticallygenerated by a motion planning algorithm such as a rapidly exploringrandom tree (RRT) and based on given data indicating a hit pointposition on a workpiece and workpiece or jig shape data given as CADdata (for example, Re-publication of PCT International Publication No.2017/119088 A1). Also present is an algorithm automatically generating amotion path based on a robot motion model.

Created in a case where the robot's movement path is automaticallygenerated is, for example, a motion path for sequentially moving therobot to the hit point position where welding should be performed whileavoiding the workpiece or a jig that hinders the robot's motion in thespot welding. In a case where the motion path is automaticallygenerated, however, the robot's posture at the previous hit pointposition, the robot's efficient movement during workpiece or jigavoidance, and the load that is applied to the robot's axis (joint), andthe like are not considered. A skilled worker manually teaches the robota motion path in view of various such factors affecting the robot'sefficient motion. The manual teaching, however, is burdensome on theworker's part. Besides, the manual teaching is time-consuming, and thusa problem arises in the form of an increase in cycle time. This problemarises in the other robots including handling robots as well as weldingrobots.

An object of the present disclosure is to provide an automatic pathgeneration device automatically generating an efficient motion path fora robot motion.

SUMMARY OF THE INVENTION

An automatic path generation device of one embodiment of the presentdisclosure automatically generates a motion path (hereinafter, referredto as temporary motion path) based on the position of a motion pointwhere a robot performs any motion (such as a hit point position in spotwelding) and the shape of an interference object such as a workpiece anda jig and by means of a motion planning algorithm such as RRT. Theautomatic path generation device has a machine learning device. Askilledworker manually creates a motion path (hereinafter, referred to asactual motion path). The machine learning device learns the correlationbetween the temporary motion path and the actual motion path based onthe actual motion path and the automatically generated motion path.Here, the motion path manually created by the skilled worker is a motionpath that the skilled worker creates based on the position of a motionpoint and the shape of an interference object identical to the positionof the motion point and the shape of the interference object that themotion planning algorithm uses in order to generate the motion path. Theautomatic path generation device estimates the actual motion path fromthe temporary motion path by using the machine learning device that haslearned the correlation between the temporary motion path and the actualmotion path. The temporary motion path is automatically generated by themotion planning algorithm and is not ideal as a motion path. The machinelearning device learns the difference between the temporary motion pathand the actual motion path and automatically estimates the actual motionpath from the temporary motion path. As a result, the automatic pathgeneration device is capable of automatically deriving an efficientmotion path.

An aspect of the present disclosure relates to an automatic pathgeneration device generating a motion path of a robot. The automaticpath generation device includes a preprocessing unit creating teacherdata based on a temporary motion path which is a motion path between aplurality of motion points where the robot moves and which isautomatically generated with a motion planning algorithm and an actualmotion path which is a motion path between the motion points and whichis created by a skilled worker and a motion path learning unitgenerating a learned model which has learned a difference between thetemporary motion path and the actual motion path with teacher datacreated by the preprocessing unit.

Another aspect of the present disclosure relates to an automatic pathgeneration device generating a motion path of a robot. The automaticpath generation device includes a learning model storage unit storing alearned model which has learned a difference between a temporary motionpath which is a motion path between a plurality of motion points wherethe robot moves and which is automatically generated with a motionplanning algorithm and an actual motion path which is a motion pathbetween the motion points and which is created by a skilled worker and amotion path estimation unit estimating an actual motion path of therobot based on a temporary motion path of the robot automaticallygenerated with a motion planning algorithm and a learned model stored inthe learning model storage unit.

With the present disclosure, it is possible to automatically generate anefficient motion path of a robot in welding performed by means of therobot.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects and features of the present disclosure willbecome apparent from the following description of examples withreference to accompanying drawings, in which:

FIG. 1 is a schematic hardware configuration diagram of an automaticpath generation device according to one embodiment;

FIG. 2 is a schematic functional block diagram of an automatic pathgeneration device according to a first embodiment;

FIG. 3 is a diagram exemplifying a robot motion path;

FIG. 4 is a schematic functional block diagram of an automatic pathgeneration device according to another modification example;

FIG. 5 is a schematic functional block diagram of an automatic pathgeneration device according to another modification example;

FIG. 6 is a diagram exemplifying a robot motion path from an actualmotion path;

FIG. 7 is a schematic functional block diagram of an automatic pathgeneration device according to a second embodiment; and

FIG. 8 is a schematic functional block diagram of an automatic pathgeneration device according to a third embodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Hereinafter, embodiments of the present disclosure will be describedwith reference to accompanying drawings. An automatic path generationdevice according to the present embodiment will be described below as adevice generating a robot's motion path in spot welding. In this case,the position of the robot's motion point is, for example, a hit pointposition in the spot welding and interference objects include aworkpiece and a jig.

FIG. 1 is a schematic hardware configuration diagram illustrating theautomatic path generation device according to one embodiment. Anautomatic path generation device 1 is implemented in, for example, acontroller controlling the robot. The automatic path generation device 1is implemented in a personal computer put aside the robot and the robotcontroller, and a computer such as a cell computer, a host computer, anedge server, and a cloud server connected to the robot controller bymeans of a wired/wireless network. In the present embodiment, theautomatic path generation device 1 is implemented in the controllercontrolling the robot.

The automatic path generation device 1 of the present embodiment has afunction used for general robot control. The automatic path generationdevice 1 is connected to a robot 2 via an interface 19 and controls therobot 2. The robot 2 has at least one link (movable portion) and atleast one joint. The robot 2 is, for example, a six-axis articulatedtype robot. The robot 2 includes a tool such as a C gun, an X gun, and alaser for performing welding such as spot welding, arc welding, andlaser welding. The robot 2 may grip the workpiece and weld the workpieceby changing the position of the gripped workpiece with respect to thewelding tool fixed at a predetermined position.

The automatic path generation device 1 includes a machine learningdevice 100 in which the motion path of the robot 2 during the welding ismachine-learned in advance. The automatic path generation device 1controls the robot 2 and performs the workpiece welding in accordancewith the result of estimation of the optimum motion path of the robot 2output from the machine learning device 100 based on the result of themachine learning. The detailed configuration of the robot 2 is alreadyknown, and thus will not be described in detail in the presentspecification.

A CPU 11 of the automatic path generation device 1 is a processorcontrolling the automatic path generation device 1 as a whole. The CPU11 reads the system program that is stored in a ROM 12 via a bus 20. TheCPU 11 controls the entire automatic path generation device 1 inaccordance with the read system program. A RAM 13 temporarily storestemporary calculation and display data, various data input by a workervia an input device 71, and the like.

A memory, an SSD, or the like backed up by a battery (not illustrated)constitutes a nonvolatile memory 14. Accordingly, the storage state ofthe nonvolatile memory 14 is maintained even after the automatic pathgeneration device 1 is turned off. The nonvolatile memory 14 has asetting area in which setting information related to the operation ofthe automatic path generation device 1 is stored. The nonvolatile memory14 stores, for example, CAD data or a control program for the robot 2input from the input device 71 and CAD data or a control program for therobot 2 read from an external storage device (not illustrated). Theprogram and various data stored in the nonvolatile memory 14 may beloaded in the RAM 13 during execution/use. Various system programs(including a system program for controlling communication with themachine learning device 100 (described later)) such as a known analysisprogram are pre-written in the ROM 12.

A display device 70 displays, for example, each data read into thememory and data obtained as a result of program execution. Data and thelike output from the machine learning device 100 (described later) areinput to the display device 70 via an interface 17 and displayed by thedisplay device 70. A keyboard, a pointing device, and the likeconstitute the input device 71. The input device 71 receives data, acommand, and the like based on an operation conducted by the worker anddelivers the data, the command, and the like to the CPU 11 via aninterface 18.

An interface 21 is an interface for connecting the machine learningdevice 100 with each unit of the automatic path generation device 1. Themachine learning device 100 includes a processor 101 controlling themachine learning device 100 as a whole and a ROM 102 storing a systemprogram and the like. The machine learning device 100 further includes aRAM 103 for performing temporary storage in each processing related tomachine learning and a nonvolatile memory 104 used for a learning modelor the like to be stored. The machine learning device 100 observes eachpiece of information (such as the room temperature that is set via theinput device 71 and the state quantity of each motor that is acquiredfrom the robot 2) that can be acquired by the automatic path generationdevice 1 via the interface 21. Each unit of the automatic pathgeneration device 1 acquires a processing result from the machinelearning device 100 via the interface 21.

FIG. 2 is a schematic functional block diagram in the learning mode ofthe machine learning device 100 of the automatic path generation device1 according to a first embodiment. The function of each functional blockillustrated in FIG. 2 is realized by the CPU 11 of the automatic pathgeneration device 1 illustrated in FIG. 1 and the processor 101 of themachine learning device 100 executing the respective system programs ofthe CPU 11 and the processor 101 and controlling the operation of eachunit of the automatic path generation device 1 and the machine learningdevice 100.

The automatic path generation device 1 includes a control unit 30, apreprocessing unit 34, and a motion path learning unit 38. The controlunit 30 controls the robot 2. The preprocessing unit 34 creates teacherdata used for the machine learning that is executed by the machinelearning device 100. The teacher data is created based on a controlprogram 50 including information indicating a motion path created byteaching from the worker and CAD data 52 including shape information onthe workpiece and the jig to be welded. The motion path learning unit 38learns the motion path of the robot 2 by using the teacher data createdby the preprocessing unit 34.

The control unit 30 controls the robot 2 based on the control operationthat the worker performs on an operation board (not illustrated), thecontrol program that is stored in the nonvolatile memory 14 or the like,the motion path that is output from the machine learning device 100, orthe like. The control unit 30 has a function for general controlrequired for each unit of the robot 2 to be controlled. In a case whereeach axis (joint) of the robot 2 is moved, for example, the control unit30 outputs command data indicating the amount of change in axis anglefor each control cycle to the motor that drives the axis. The controlunit 30 acquires the motor state quantities (such as the current value,the position, the velocity, the acceleration, and the torque) of eachmotor of the robot 2 and uses the acquired motor state quantities incontrolling the robot 2.

The preprocessing unit 34 is functional means for creating teacher dataT used for supervised learning, which is a machine learning method,based on the control program 50 and the CAD data 52 and outputting theteacher data T to the machine learning device 100. The control program50 includes information indicating a motion path created by teachingfrom a skilled worker. The CAD data 52 includes the shape information onthe workpiece and the jig to be welded. The preprocessing unit 34calculates the motion path of the robot 2 (hereinafter, referred to astemporary motion path) by using a known motion planning algorithm (suchas an RRT) and based on the spot welding hit point position informationextracted from the control program 50 and the workpiece and jig positioninformation included in the CAD data 52. The preprocessing unit 34creates the teacher data and outputs the teacher data to the machinelearning device. In the teacher data, data related to the calculatedmotion path is input data and output data is data related to the motionpath extracted from the control program 50 and created by teaching fromthe worker (hereinafter, referred to as actual motion path). Each of thetemporary motion path and the actual motion path is, for example, hitpoint-related information, information related to an inter-hit pointmidpoint, and time series data on the operation parameters of the robot2 between the hit point and the midpoint. The motion parameters are, forexample, time series data on the motion parameters indicating thevelocity, the acceleration, the smoothness, and the like of each axis.Each of the temporary motion path and the actual motion path is hitpoint-related information, information related to an inter-hit pointmidpoint, and time series data on the motion parameters of the toolgripped by the robot 2 between the hit point and the midpoint. Themotion parameters in this case may be, for example, time series data onthe motion parameters indicating, for example, the velocity, theacceleration, and the smoothness of the position of the tool gripped bythe robot 2.

FIG. 3 is a diagram illustrating an example of the motion path formoving between the hit point positions of the workpiece. In FIG. 3, thewelding tool is attached to the hand of the robot 2. The welding toolperforms welding on hit point positions P1 and P2 of the workpiece underthe control of the control unit 30. When the welding tool is moved fromthe hit point position P1 to the hit point position P2, a motion paththat does not interfere with the jig needs to be generated. FIG. 3illustrates a motion path in which the welding tool sequentially movesto the hit point position P1, a midpoint P1-1, a midpoint P1-2, and thehit point position P2. In a case where each axis of the robot 2 moves inaccordance with the three motion parameters of velocity, acceleration,and smoothness (in position) during the movement between the respectivehit point and midpoint positions, the motion path from the hit pointposition P1 to the hit point position P2 is defined by the position ofeach axis of the robot 2 (or the position of the tool) at each hit pointand midpoint position, the velocity, the acceleration, and thesmoothness of each axis during the movement from the hit point positionP1 to the midpoint P1-1, the velocity, the acceleration, and thesmoothness of each axis during the movement from the midpoint P1-1 tothe midpoint P1-2, and the velocity, the acceleration, and thesmoothness of each axis during the movement from the midpoint P1-2 tothe hit point position P1-2. Such time series data is created withregard to the temporary motion path and the actual motion path. Theteacher data T is created by the temporary motion path being used asinput data and the actual motion path being used as output data.

The preprocessing unit 34 may create the single teacher data T from theentire motion path (temporary and actual motion paths) of the robot 2.The preprocessing unit 34 may create single teacher data Ti from thepartial motion path between two hit point positions Pi and Pj in theentire motion path (temporary and actual motion paths) and create aplurality of teacher data T1 to Tn (n being a positive integer)respectively corresponding to the partial motion path from the entiremotion path of the robot 2. The preprocessing unit 34 may create teacherdata as follows. The preprocessing unit 34 creates teacher data Tj inwhich the partial motion path between four hit point positions Pi to P1in the entire temporary motion path is single input data and the partialmotion path between Pj and Pk in the actual motion path is single outputdata corresponding to the input data. The preprocessing unit 34 createsthe plurality of teacher data T1 to Tn (n being a positive integer)respectively corresponding to the partial motion path from the entiremotion path of the robot 2 (In a case where the teacher data is createdin this manner, a predetermined fixed value such as 0 may be defined forpartial input data that cannot be defined from the temporary motion pathat the movement initiation and termination positions of the robot 2). Asdescribed above, the teacher data may be appropriately set in accordancewith how the learning model is defined.

The motion path learning unit 38 is functional means for performingsupervised learning using the teacher data created by the preprocessingunit 34 and generating (learning) a learned model used for estimatingthe actual motion path from the temporary motion path. The motion pathlearning unit 38 may use, for example, a neural network as a learningmodel. In this case, the motion path learning unit 38 performssupervised learning in which data related to the temporary motion pathincluded in the teacher data created by the preprocessing unit 34 isinput data and data related to the actual motion path is output data. Aneural network provided with the three layers of an input layer, anintermediate layer, and an output layer may be used as the learningmodel. A so-called deep learning method using a neural network of threeor more layers may be used as the learning model. Effective learning andinference are performed as a result.

The learning that is conducted by the motion path learning unit 38 isperformed in a case where the automatic path generation device 1functions as a learning mode. In the learning mode, the automatic pathgeneration device 1 acquires various control programs 50 and CAD data 52via an external device such as a USB device (not illustrated) or awired/wireless network (not illustrated). The automatic path generationdevice 1 performs learning based on the various acquired controlprograms 50 and CAD data 52. The automatic path generation device 1generates the learned model for estimating the actual motion path fromthe temporary motion path. Then, the learned model generated by themotion path learning unit 38 is stored in a learning model storage unit46 provided on the nonvolatile memory 104. The learned model is used forthe estimation of the actual motion path by a motion path estimationunit 40 (described later).

The automatic path generation device 1 configured as described abovegenerates, based on the control program 50 and the CAD data 52, thelearned model that has learned the actual motion path (motion pathcreated by teaching from the skilled worker) corresponding to thetemporary motion path (motion path automatically created by the motionplanning algorithm or the like). The control program 50 includesinformation indicating the motion path created by teaching from theskilled worker. The CAD data includes the shape information on theworkpiece and the jig to be welded.

A modification example of the automatic path generation device 1 of thepresent embodiment is illustrated in FIG. 4. The preprocessing unit 34creates the teacher data T based on the control program 50 including themotion path created by teaching from the skilled worker and a temporarymotion path data 54 including a motion path automatically created by amotion path algorithm or the like and outputs the teacher data T to themachine learning device 100. In this case, the worker creates thetemporary motion path data 54 based on the hit point position and theCAD data by using an external device or the like and the createdtemporary motion path data 54 is read by the automatic path generationdevice 1 along with the control program 50. Generated as a result is thelearned model that has learned the actual motion path (motion pathcreated by teaching from the skilled worker) corresponding to thetemporary motion path (motion path automatically created by the motionplanning algorithm or the like).

Another modification example of the automatic path generation device 1of the present embodiment is illustrated in FIG. 5. The preprocessingunit 34 creates the teacher data T based on the control program 50including the motion path created by teaching from the skilled workerand outputs the teacher data T to the machine learning device 100. Asexemplified in FIG. 6, the preprocessing unit 34 in this case analyzesthe actual motion path extracted from the control program 50 and createdby teaching from the skilled worker. In a case where the path betweenthe hit point position Pi and the hit point position Pj in the actualmotion path is a bypass path (that is, in a case where the path is not asubstantially straight line), the preprocessing unit 34 assumes that avirtual interference object is present at the position of an (preset)inside distance a of the motion path. The preprocessing unit 34calculates the temporary motion path of the robot 2 by using a knownmotion planning algorithm (such as an RRT) in view of the assumedvirtual interference object. The teacher data T is created in which thetemporary motion path obtained as described above and the actual motionpath are input data and output data, respectively. The teacher data T isoutput to the machine learning device 100. The preprocessing unit 34 maybe configured as a machine learner that has learned the temporary motionpath corresponding to the actual motion path. In this case, thepreprocessing unit 34 estimates the temporary motion path based on theactual motion path extracted from the control program 50. Thepreprocessing unit 34 creates teacher data in which the estimatedtemporary motion path is input data and outputs the teacher data to themachine learning device 100. With this configuration, it is possible togenerate the learned model that has learned the actual motion path(motion path created by teaching from the skilled worker) correspondingto the temporary motion path (motion path automatically created by themotion planning algorithm or the like) is learned even in a case whereonly the control program 50 is present with the CAD data 52 absent.

FIG. 7 is a schematic functional block diagram in the estimation mode ofthe machine learning device 100 of the automatic path generation device1 according to a second embodiment. Each functional block illustrated inFIG. 7 is realized by the CPU 11 of the automatic path generation device1 illustrated in FIG. 1 and the processor 101 of the machine learningdevice 100 executing the respective system programs of the CPU 11 andthe processor 101 and controlling the operation of each unit of theautomatic path generation device 1 and the machine learning device 100.

In the estimation mode, the automatic path generation device 1 of thepresent embodiment estimates the actual motion path based on thetemporary motion path data 54. The automatic path generation device 1controls the robot 2 based on the estimated actual motion path. Thecontrol unit 30 in the automatic path generation device 1 according tothe present embodiment is similar in function to the control unit 30 inthe automatic path generation device 1 according to the firstembodiment.

The motion path estimation unit 40 performs actual motion pathestimation using the learned model stored in the learning model storageunit 46 based on the temporary motion path data 54 acquired via anexternal device such as a USB device (not illustrated) or awired/wireless network (not illustrated). As for the motion pathestimation unit 40 of the present modification example, the learnedmodel is the (parameter-determined) neural network generated by thesupervised learning conducted by the motion path learning unit 38. Themotion path estimation unit 40 estimates (calculates) the actual motionpath by inputting the temporary motion path data 54 as input data to thelearned model. The actual motion path estimated by the motion pathestimation unit 40 is output to the control unit 30 and used for therobot 2 to be controlled. In addition, the actual motion path estimatedby the motion path estimation unit 40 may be used after, for example,display output to the display device 70 or transmission output to a hostcomputer, a cloud computer, or the like via a wired/wireless network(not illustrated).

The automatic path generation device 1 of the present embodimentconfigured as described above performs motion path learning based on aplurality of teacher data obtained by the robot 2 performing motions ofvarious patterns. The automatic path generation device 1 estimates anefficient motion path of the robot 2 by using a learned model in which asufficient learning result is obtained.

FIG. 8 is a schematic functional block diagram in the estimation mode ofthe machine learning device 100 of the automatic path generation device1 according to a third embodiment. The function of each functional blockillustrated in FIG. 8 is realized by the CPU 11 of the automatic pathgeneration device 1 illustrated in FIG. 1 and the processor 101 of themachine learning device 100 executing the respective system programs ofthe CPU 11 and the processor 101 and controlling the operation of eachunit of the automatic path generation device 1 and the machine learningdevice 100. The control program 50, the CAD data 52, the temporarymotion path data 54, and the like are not illustrated in FIG. 8.

The automatic path generation device 1 of the present embodimentincludes a simulation unit 42 simulating the motion of the robot 2. Thesimulation unit 42 performs the simulation using the actual motion paththat the motion path estimation unit 40 estimates based on the temporarymotion path data 54 in the estimation mode. As a result of the motionsimulation by the simulation unit 42, interference may occur between therobot 2 or the welding tool and the workpiece or the jig on theestimated actual motion path. In this case, a motion path correctionunit 44 changes the position of the midpoint of the estimated actualmotion path or the like to a position where no interference occursbetween the robot 2 or the welding tool and the workpiece or the jig(such as a position away by a predetermined distance set in advance fromthe object of interference). The changed actual motion path is input tothe preprocessing unit 34. The preprocessing unit 34 creates new teacherdata T with the input actual motion path and the temporary motion pathused for the actual motion path estimation. The new teacher data T isused for relearning with respect to the learned model.

The automatic path generation device 1 of the present embodimentcorrects the actual motion path such that no interference occurs in acase where interference occurs on the estimated actual motion path. Theautomatic path generation device 1 constructs a more appropriate learnedmodel by performing relearning by using the corrected actual motionpath.

Although embodiments of the present disclosure have been describedabove, the present disclosure is not limited to the examples of theembodiments described above and can be implemented in various aspects bybeing changed as appropriate.

For example, the learning and arithmetic algorithms executed by themachine learning device 100, the control algorithm executed by theautomatic path generation device 1, and the like are not limited tothose described above and various algorithms can be adopted.

According to the description of the embodiments described above, theautomatic path generation device 1 and the machine learning device 100are devices having different CPUs. Alternatively, the machine learningdevice 100 may be realized by the system program stored in the ROM 12and the CPU 11 of the automatic path generation device 1.

In the embodiments exemplified above, the automatic path generationdevice 1 estimates the inter-hit point motion path in spot welding. Theautomatic path generation device 1 is capable of estimating a motionpath at an air cut part in arc welding, laser welding, and the like aswell. In other words, the automatic path generation device 1 is capableof estimating a motion path related to a welding tool movement betweenpreceding and subsequent welding processes.

Also possible is application to automatic path generation for generalrobots. For example, in a handling robot (transport robot), the positionof the motion point is a workpiece gripping position, a pre-workpiecetransport position, or the like. In the handling robot, the interferenceobject is, for example, another device present on a transport path. Theautomatic path generation device 1 estimates, for example, a motion pathuntil workpiece gripping and a motion path during the workpiece movementto a movement destination that is subsequent to the workpiece gripping.In other words, the automatic path generation device 1 learns anefficient actual motion path created by a skilled worker correspondingto the temporary motion path automatically generated by the motionplanning algorithm or the like and estimates an efficient actual motionpath from the temporary motion path automatically generated by themotion planning algorithm or the like by using the result of thelearning.

1. An automatic path generation device generating a motion path of arobot, the automatic path generation device comprising: a preprocessingunit creating teacher data based on a temporary motion path which is amotion path between a plurality of motion points where the robot movesand which is automatically generated with a motion planning algorithmand an actual motion path which is a motion path between the motionpoints and which is created by a skilled worker; and a motion pathlearning unit generating a learned model which has learned a differencebetween the temporary motion path and the actual motion path withteacher data created by the preprocessing unit.
 2. The automatic pathgeneration device according to claim 1, wherein the preprocessing unitextracts the actual motion path from a control program created by askilled worker.
 3. The automatic path generation device according toclaim 2, wherein the preprocessing unit generates the temporary motionpath by extracting the motion point from the control program andexecuting a motion planning algorithm using the extracted motion pointand shape information on an interference object in a motion environmentof the robot.
 4. The automatic path generation device according to claim3, wherein shape information on an interference object in a motionenvironment of the robot is CAD data.
 5. The automatic path generationdevice according to claim 3, wherein shape information on aninterference object in a motion environment of the robot is estimatedbased on an actual motion path extracted from the control program.
 6. Anautomatic path generation device generating a motion path of a robot,the automatic path generation device comprising: a learning modelstorage unit storing a learned model which has learned a differencebetween a temporary motion path which is a motion path between aplurality of motion points where the robot moves and which isautomatically generated with a motion planning algorithm and an actualmotion path which is a motion path between the motion points and whichis created by a skilled worker; and a motion path estimation unitestimating an actual motion path of the robot based on a temporarymotion path of the robot automatically generated with a motion planningalgorithm and a learned model stored in the learning model storage unit.7. The automatic path generation device according to claim 6, furthercomprising: a simulation unit simulating a motion of the robot based onan actual motion path of the robot estimated by the motion pathestimation unit; and a motion path correction unit correcting the actualmotion path in a case where the robot interferes with an interferenceobject as a result of the simulation such that the interference does notoccur, wherein relearning of the learned model is performed based on anactual motion path corrected by the motion path correction unit.